Use DO on different WML locations
With the significant growth of Internet traffic these days, some network connections might fail or be slower. This post shows how you can easily get your Decision Optimization (DO) models deployed and running on different Watson Machine Learning (WML) locations.
As everyone talks about how each technology, product or company can help with the Covid-19 crisis, I do think the best thing DO can do is simply to continue to support everyday decisions as it has been doing for years. DO is used for supply chain design and operations, production planning and scheduling, energy production and transportation, nurse scheduling, etc in thousands of places. DO can easily adapt to new situations by simple changes in the input data or in the model formulations. But first, it just needs to continue to operate. In this post, I show how can DO models can be deployed on different clusters in different regions of the world.
DO for WML is currently supported on 4 different locations: Dallas, London, Frankfurt and Tokyo.
Know your location
All models and deployments are attached to one single WML instance which is attached to a given location. The url included in all credentials of your instance lets you know the location you are using. For example, if you are using London, your url will look like:
"url": "https://eu-gb.ml.cloud.ibm.com"And if you are using Dallas, your urlwill look like:
"url": "https://us-south.ml.cloud.ibm.com"List all your instances
Depending on your plan, you can create different instances on the same location and/or different locations. A good way to know all your instances is to use the resource list from IBM cloud https://cloud.ibm.com/resources. WML instances appear under Services and are listed in the Machine Learning offering.
Create a new instance
In order to create a new instance, look for Machine Learning in the catalog: https://cloud.ibm.com/catalog/services/machine-learning and in the Region menu, choose the location to use.
Then choose your plan (Lite, Standard or Professional) and select Create.
Get new credentials and use them
For the newly created instance, just go to Service credentials and generate some for this new instance.
You can then use these credentials in your notebook, your custom code or in your production application. You will need to recreate, reupload and redeploy your model. and get a new deployment id. See how to do this from Python, from Java or using an OPL model.
You have seen this is very easy to create different instances and use them in your code. You might want to create a Standard Plan instance on some other location to try. Standard plan instances have no cost when not used.
